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        五：集成方法 (Ensemble Methods) - Fainle的博客 | Fainle&#39;s Blog
        
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        五：集成方法 (Ensemble Methods)
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        <span class="attr">发布于：<span>2020-07-25 08:00</span></span>
        
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        <a class="tag" href="/tags/#统计机器学习笔记" title="统计机器学习笔记">统计机器学习笔记</a>
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        <span class="attr">访问：<span id="busuanzi_value_page_pv"></span>
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        <h3 id="一-概述"><a href="#一-概述" class="headerlink" title="一 . 概述"></a>一 . 概述</h3><p>集成方法是将多个模型组合起来获得更好的模型的一种方法。一般分为 bagging 和 boosting。</p>
<h3 id="二-所属分类"><a href="#二-所属分类" class="headerlink" title="二 . 所属分类"></a>二 . 所属分类</h3><p>集成方法属于 监督学习。</p>
<h3 id="三-bagging"><a href="#三-bagging" class="headerlink" title="三 . bagging"></a>三 . bagging</h3><p>装袋算法, 对多个模型的结果投票，平均等方式获得结果。</p>
<p>代表算法:</p>
<h4 id="1-随机森林-Random-Forests"><a href="#1-随机森林-Random-Forests" class="headerlink" title="1 . 随机森林 (Random Forests)"></a>1 . 随机森林 (Random Forests)</h4><p>随机森林是一个包含多个决策树的分类器，并且其输出的类别是由个别树输出的类别的众数而定。</p>
<h3 id="四-boosting。"><a href="#四-boosting。" class="headerlink" title="四 . boosting。"></a>四 . boosting。</h3><p>提升方法</p>
<p>多个弱分类器组合成一个强分类器的算法，通常给不同的弱分类器不同的权重。数据会被重新加权。</p>
<p>代表算法:</p>
<h4 id="1-AdaBoost"><a href="#1-AdaBoost" class="headerlink" title="1 . AdaBoost"></a>1 . AdaBoost</h4><p>自适应增强</p>
<p>AdaBoost方法的自适应在于：前一个分类器分错的样本会被用来训练下一个分类器。</p>
<p>其中权重计算公式 </p>
<script type="math/tex; mode=display">weight = ln(\frac{accuracy}{1-accuracy})</script><p>例子<br><figure class="highlight nix"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">from sklearn.ensemble <span class="built_in">import</span> AdaBoostClassifier</span><br><span class="line">from sklearn.tree <span class="built_in">import</span> DecisionTreeClassifier</span><br><span class="line"></span><br><span class="line"><span class="attr">model</span> = AdaBoostClassifier(<span class="attr">base_estimator</span> = DecisionTreeClassifier(<span class="attr">max_depth=2),</span> <span class="attr">n_estimators</span> = <span class="number">4</span>) <span class="comment"># base_estimator 选择的弱模型，n_estimators 模型的数量</span></span><br><span class="line"></span><br><span class="line">model.fit(x_train, y_train)</span><br><span class="line">model.predict(x_test)</span><br></pre></td></tr></table></figure></p>

        
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